Method, apparatus, computing device and computer-readable storage medium for identifying signal

ABSTRACT

It is disclosed a method, an apparatus, a computing device, and a computer-readable storage medium for identifying a signal. The method includes demodulating a modulated signal to generate a transmission signal, transmitting the transmission signal, receiving an echo signal generated by a reflection of the transmission signal, demodulating the echo signal to obtain demodulated information, identifying the demodulated information by using a target network model to obtain an identification result of the echo signal, and outputting the identification result to a graphical user interface for display.

RELATED APPLICATIONS

This application claims priority to and the benefit of Chinese PatentApplication No. 202110351582.8, filed on Mar. 31, 2021, the content ofwhich is incorporated in its entirety herein by reference.

FIELD

This application relates to the field of signal detection, and inparticular to a method, an apparatus, a computing device, and acomputer-readable storage medium for identifying a signal.

BACKGROUND

In related technologies, the identification of vital signs of a livingbody mainly relies on contact style signal detection apparatuses thatare attached to the living body, such as sensors, electrodes, etc., toobtain relevant information. However, the use of the contact stylesignal detection apparatuses are restricted at a relatively high degreein the clinical dynamic monitoring of infectious disease patients,severely burned patients, elderly people, and infants.

Also, in related technologies, a signal identification model based ondeep learning can be used to identify signals. However, the constructionof such a signal identification model based on deep learning requires alarge amount of parameters, and the complexity of the model isrelatively high, such that the model has a large computational load andlow identification efficiency. Therefore, it is often necessary toinstall such a model into large-scale hardware devices, instead ofportable devices which has relatively weak computing capacity. Thisleads to poor flexibility in the detection of vital signs.

SUMMARY

According to an aspect of this application, there is provided a methodfor identifying a signal, comprising: demodulating a modulated signal togenerate a transmission signal; transmitting the transmission signal;receiving an echo signal generated by a reflection of the transmissionsignal; demodulating the echo signal to obtain demodulated information;identifying the demodulated information by using a target network modelto obtain an identification result of the echo signal; and outputtingthe identification result to a graphical user interface for display.

In some embodiments, demodulating the echo signal to obtain thedemodulated information comprises: performing quadrature demodulation onthe echo signal to obtain a time-domain signal. Also, identifying thedemodulated information by using the target network model to obtain theidentification result of the echo signal comprises: identifying thetime-domain signal by using the target network model to obtain theidentification result of the echo signal.

In some embodiments, demodulating the echo signal to obtain thedemodulated information comprises: performing quadrature demodulation onthe echo signal to obtain a time-domain signal and performing a FastFourier Transform on the time-domain signal to obtain frequency-domaininformation. Also, identifying the demodulated information by using thetarget network model to obtain the identification result of the echosignal comprises: identifying the time-domain signal and thefrequency-domain information by using the target network model to obtainthe identification result of the echo signal.

In some embodiments, demodulating the echo signal to obtain thedemodulated information comprises: performing quadrature demodulation onthe echo signal to obtain a time-domain signal; and performing a FastFourier Transform on the time-domain signal to obtain frequency-domaininformation. Also, identifying the demodulated information by using thetarget network model to obtain the identification result of the echosignal comprises: identifying the frequency-domain information by usingthe target network model to obtain the identification result of the echosignal.

In some embodiments, the target network model comprises a lightweightneural network model and a classifier, and identifying the demodulatedinformation by using the target network model to obtain theidentification result of the echo signal comprises: inputting thedemodulated information into the lightweight neural network model toobtain feature data; and inputting the feature data into the classifierto obtain the identification result.

In some embodiments, the lightweight neural network model is trainedtraining by following steps: obtaining sample signal data and labelscorresponding to the sample signal data; inputting the sample signaldata into an untrained lightweight neural network model for a firsttraining of supervised learning to obtain sample feature predictiondata; determining a first loss function based on the sample featureprediction data and the labels corresponding to the sample signal data;performing one or more first iterations on the untrained lightweightneural network model according to a first loss data calculated by thefirst loss function; in response to a number of the first iterationsreaching a first preset number, stopping the first iterations to obtainthe lightweight neural network model.

In some embodiments, the classifier is trained by following steps:obtaining sample feature data output by the lightweight neural networkmodel; inputting the sample feature data into an untrained classifierfor a second training of supervised learning to obtain a sampleidentification prediction result; determining a second loss functionbased on the sample identification prediction result and the labelscorresponding to the sample signal data; performing one or more seconditerations on the untrained classifier according to a second loss datacalculated by the second loss function; in response to a number of thesecond iterations reaching a second preset number, stopping the seconditerations to obtain the classifier.

In some embodiments, inputting the demodulated information into thelightweight neural network model to obtain the feature data comprises:receiving a control instruction; receiving and caching a weight datastream and a feature map data stream according to the controlinstruction; windowing the weight data stream to obtain a first windowedweight data sub-stream of a first quantity of channels and a secondwindowed weight data sub-stream of a second quantity of channels;windowing the feature map data stream to obtain a windowed feature mapdata sub-stream of a third quantity of channels, wherein the thirdquantity is equal to the first quantity; performing a first convolutionprocessing on the windowed feature map data sub-stream by using thefirst windowed weight data sub-stream to obtain an intermediate datastream of a plurality of channels; performing a second convolutionprocessing on the intermediate data stream of the plurality of channelsby using the second windowed weight data sub-stream to obtain an outputdata stream; generating the feature data based on the output datastream.

According to another aspect of this application, there is provided anapparatus for identifying a signal. The apparatus comprises atransmission signal generating module, configured to demodulate amodulated signal to generate a transmission signal; a signaltransmitting module, configured to transmit the transmission signal; anecho signal receiving module, configured to receive an echo signalgenerated by a reflection of the transmission signal; an echo signaldemodulating module, configured to demodulate the echo signal to obtaindemodulated information; an identification module, configured toidentify the demodulated information by using a target network model toobtain an identification result of the echo signal; a display module,configured to output the identification result to a graphical userinterface for display.

In some embodiments, the echo signal demodulating module is configuredto perform quadrature demodulation on the echo signal to obtain atime-domain signal.

In some embodiments, the echo signal demodulating module is configuredto perform a Fast Fourier Transform on the time-domain signal to obtainfrequency-domain information.

In some embodiments, the identification module comprises a serialcontrol unit and a parallel acceleration unit, wherein the serialcontrol unit is configured to control the parallel acceleration unit,and the parallel acceleration unit is configured to achieve a parallelconvolution calculation.

In some embodiments, the serial control unit comprises a flow controlsub-unit, a weight data sub-unit, a pooling function sub-unit, and anactivation function sub-unit.

In some embodiments, the parallel acceleration unit comprises aninstruction control sub-unit, a cache sub-unit, a weight windowgeneration sub-unit, a feature map window generation sub-unit, aconvolution sub-unit, an output cache sub-unit, and an output sub-unit.

In some embodiments, the identification module further comprises aclassification unit, wherein the classification unit is configured toidentify the demodulated information to obtain the identification resultof the echo signal.

According to another aspect of this application, there is provided acomputing device, comprising: a memory configured to storecomputer-executable instructions; and a processor configured to executethe computer-executable instructions to cause the computing device toperform the method according to any of the embodiments of thisapplication.

According to another aspect of this application, there is provided acomputer-readable storage medium, comprising computer-executableinstructions that when executed by a processor of a computing devicecause the processor to perform the method according to any of theembodiments of this application.

BRIEF DESCRIPTION OF THE DRAWINGS

By reading the detailed description of the non-limiting embodiments withreference to the following drawings, other features, purposes andadvantages of the present application will become more apparent.

FIG. 1 schematically shows a flowchart of a method for identifying asignal according to an embodiment of the present application.

FIG. 2 schematically shows a structural block diagram of a computingdevice according to an embodiment of the present application.

FIG. 3 schematically shows a structural block diagram of an apparatusfor identifying a signal according to an embodiment of the presentapplication.

FIG. 4 schematically shows a signal flow diagram inside an apparatus foridentifying a signal according to an embodiment of the presentapplication.

FIG. 5 schematically shows an exemplary display image of a graphicaluser interface of a display module.

FIG. 6a-6c schematically show a flowchart of a method for identifying asignal according to an embodiment of the present application.

FIG. 7 schematically shows a signal processing process according to anembodiment of the present application.

FIG. 8 schematically shows a flowchart of a method for identifying asignal according to an embodiment of the present application.

FIG. 9 schematically shows a flowchart of a process of training alightweight neural network model according to an embodiment of thepresent application.

FIG. 10 schematically shows a network structure of a target networkmodel according to an embodiment of the present application.

FIG. 11 schematically shows a flowchart of a process of training aclassifier according to an embodiment of the present application.

FIG. 12 schematically shows the transplantation process of the trainedtarget network model.

FIG. 13 schematically shows a flowchart of a method for identifying asignal according to an embodiment of the present application.

FIG. 14 schematically shows a block diagram of the internal structure ofthe identification module of an apparatus for identifying a signalaccording to an embodiment of the present application.

FIG. 15 schematically shows the power circuit design of theidentification module of an apparatus for identifying a signal accordingto an embodiment of the present application.

FIG. 16 schematically shows the circuit design of the peripheralcomponent interconnect express of the identification module of theapparatus for identifying a signal according to an embodiment of thepresent application.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The embodiments of the present application are described in detailbelow. Examples of the embodiments are shown in the accompanyingdrawings, in which the same or similar reference numerals indicate thesame or similar elements or elements with the same or similar functions.The following embodiments described with reference to the drawings areexemplary, and are only used to explain the present application, andcannot be understood as a limitation to the present application.

FIG. 1 schematically shows a flowchart of a method for identifying asignal according to an embodiment of the present application. As shownin FIG. 1, this method comprises the following steps:

at step S10, demodulating a modulated signal to generate a transmissionsignal;

at step S20, transmitting the transmission signal;

at step S30, receiving an echo signal generated by a reflection of thetransmission signal;

at step S40, demodulating the echo signal to obtain demodulatedinformation;

at step S50, identifying the demodulated information by using a targetnetwork model to obtain an identification result of the echo signal;

at step S60, outputting the identification result to a graphical userinterface for display.

FIG. 2 schematically shows a structural block diagram of a computingdevice 100 according to an embodiment of the present application. Thecomputing device 100 comprises a processor 102 and a memory 104. Thememory 104 stores computer-executable instructions 106. When thecomputer-executable instructions 106 are executed by the processor 102,they cause the computing device 100 to execute the method foridentifying a signal according to the embodiment of the presentapplication. More specifically, the processor 102 may be used todemodulate the modulated signal to generate a transmission signal,transmit the transmission signal, receive the echo signal generated bythe reflection of the transmission signal, demodulate the echo signal toobtain demodulated information, identify the demodulated information byusing a target network model to obtain an identification result of theecho signal, and output the identification result to the graphical userinterface for display.

FIG. 3 schematically shows a structural block diagram of an apparatusfor identifying a signal 110 according to an embodiment of the presentapplication. The method for identifying a signal according to theembodiment of the present application can be implemented by theapparatus for identifying a signal 110. The apparatus for identifying asignal 110 comprises a transmission signal generating module 112, asignal transmitting module 114, an echo signal receiving module 116, anecho signal demodulating module 118, an identification module 120, and adisplay module 122. The step S10 of the method for identifying a signalaccording to the embodiment of the present application can beimplemented by the transmission signal generating module 112. The stepS20 can be implemented by the signal transmitting module 114. The stepS30 can be implemented by the echo signal receiving module 116. The stepS40 can be implemented by the echo signal demodulating module 118. Thestep S50 can be implemented by the identification module 120. The stepS60 may be implemented by the display module 122. That is, thetransmission signal generating module 112 is configured to demodulate amodulated signal to generate a transmission signal, the signaltransmitting module 114 is configured to transmit the transmissionsignal, the echo signal receiving module 116 is configured to receive anecho signal generated by a reflection of the transmission signal, theecho signal demodulating module 118 is configured to demodulate the echosignal to obtain demodulated information, the identification module 120is configured to identify the demodulated information by using a targetnetwork model to obtain the identification result of the echo signal,and the display module 122 is configured to output the identificationresult to the graphical user interface for display.

Specifically, the transmission signal generating module may generate alinear frequency modulation pulse. Different from the regular pulsesignal with fixed frequency, the signal frequency of the linearfrequency modulation pulse increases linearly with time. After thelinear frequency-modulated signal is generated, the linearfrequency-modulated signal is modulated and demodulated to apredetermined signal frequency and transmitted to the object to bemeasured. In some embodiments, the object to be measured may be a livingbody, in particular, a living body whose vital signs may be reflected bythe movement of the body surface. According to the Doppler effect, whenan object moves toward or away from a signal transmitting and receivingdevice, the frequency and phase of the linear frequency modulation pulsereflected by the object will change. Since the wavelength of the pulsesignal is very short (for example, within 4 mm), any small change of theobject (even a movement less than 1 mm) will cause a large phase changeof the signal. The small frequency changes are not easy to be detected,while large phase changes are easier to be detected. Therefore, thephase information can be used to detect the speed of the object'smovement. To determine the speed of the object, multiple linearfrequency modulation pulses are used, and the phase difference betweenthe continuously reflected linear frequency modulation pulses isrecorded, and the speed is calculated based on it. In the scenario ofdetecting vital signs, when a linear frequency modulation pulse istransmitted to a body part (for example, the chest area) of the livingbody to be measured, due to the movement of the body part (for example,the movement of the chest, which may be caused by heartbeat and/orbreathing), the reflected signal is phase modulated. The signaltransmitting module sends multiple linear frequency modulation pulsesaccording to a predetermined time interval. Each echo pulse canexperience a distance Fast Fourier Transform (FFT). By selecting thedistance level corresponding to the position of the body part, eachlinear frequency modulation pulse will record the signal phase in theselected distance level. From this, the phase change is calculated, andthus all the motion components of the motion are derived. By performingDoppler FFT to perform spectral analysis on the obtained motioncomponents, various components can be resolved. After a period oftesting, the vital signs of living bodies can be determined by analyzingthe movement of body parts.

After receiving the echo signal of the transmission signal, the echosignal is demodulated to obtain demodulated information, and a trainedtarget network model is used to identify the demodulated information toobtain the identification result, and then the identification result isoutput to the graphical user interface for display.

FIG. 4 schematically shows a diagram of the signal flow inside anapparatus for identifying a signal according to an embodiment of thepresent application. As shown in FIG. 4, the apparatus for identifying asignal 110 comprises a communication module 124, a signal processingmodule 126, an identification module 120, and a display module 122. Thetransmission signal generating module 112, the signal transmittingmodule 114, and the echo signal receiving module 116 may be integratedin the communication module 124. The signal processing module 126comprises the echo signal demodulating module 118. In addition, thesignal processing module 114 may also comprise a frequency-modulatedsignal generating module 1141. After the linear frequency-modulatedsignal is generated by the frequency-modulated signal generation module1141, the linear frequency-modulated signal is sent to the communicationmodule 124, and the transmission signal generating module 112 in thecommunication module 124 modulates and demodulates the linearfrequency-modulated signal to a predetermined signal frequency, and thenthe resulting signal is transmitted to the object to be measured by thesignal transmitting module 114. The transmission signal is reflected bythe object to be measured to obtain the echo signal, and thus the echosignal carries the information of the measured object. The echo signalreceiving module 116 of the communication module 124 receives the echosignal, modulates it to a preset frequency, and transmits it to thesignal processing module 126.

After the signal processing module 126 receives the echo signal of thetransmission signal, it uses the echo signal demodulating module 118therein to perform quadrature demodulation on the echo signal to obtainthe time-domain signal. In some embodiments, the time-domain signal canbe directly transmitted to the identification module 120, so that thesubsequent operations can be performed solely based on the time-domainsignal. In other embodiments, the time-domain signal is first convertedinto frequency-domain information through the Fast Fourier Transform,and then both the time-domain signal and frequency-domain informationare transmitted to the identification module 120 for the subsequentoperations. In other embodiments, the time-domain signal is firstconverted into frequency-domain information through the Fast FourierTransform, and then the frequency-domain information is solelytransmitted to the identification module 120 for the subsequentoperations. The identification module 120 uses the trained targetnetwork model to identify the time-domain signal and/or thefrequency-domain information to obtain the identification result, andtransmit the identification result to the display module 122. Then, thedisplay module 122 outputs the identification result to the graphicaluser interface for display.

In some more specific embodiments, the frequency-modulated signalgenerating module 1141 generates an intermediate frequency linearfrequency-modulated signal. In the field of this application, thefrequency range of the intermediate frequency linear frequency-modulatedsignal is generally 30 MHz to 300 MHz. For example, the frequency of theintermediate frequency signal may be 140 MHz. Then, the transmissionsignal generating module 112 modulates the intermediate frequency linearfrequency-modulated signal to the radio frequency. In the field of thisapplication, the frequency of the radio frequency signal is 30 GHz to300 GHz. For example, the frequency of the radio frequency signal may be64 GHz. Then, the signal transmitting module 114 sends the radiofrequency transmission signal to the object to be measured, such as aliving body. The transmission signal is reflected on the surface of theliving body to be measured to obtain the echo signal. The echo signalcarries the vital signs of the measured living body in the backtransmission and the echo signal is received by the echo signalreceiving module 116.

After receiving the echo signal of the transmission signal, the echosignal is frequency-converted, and the radio frequency echo signal isfrequency-converted to obtain an intermediate frequency echo signal.Then, the intermediate frequency echo signal is transmitted to the echosignal demodulating module 118. The echo signal demodulating module 118modulates and demodulates the intermediate frequency echo signal toobtain demodulated information such as the time-domain signal and thefrequency-domain information. In the identification module 120, thetrained target network model is used to identify the demodulatedinformation to obtain the identification result. Then, theidentification result is output to the display module 122, such as thegraphical user interface of the terminal of the smart device fordisplay.

In some embodiments, the communication module 124 has a total of fourpower supplies, which are 1.24 V digital circuit power supply, 1.24 Von-chip static random-access memory (SRAM) power supply, 1.8 V clock andinput/output pin power supply, and 3.3 V digital input/output pin powersupply. Among them, the clock input is a 40 MHz crystal oscillator, 3.3V is the input power supply, and two power supplies of 1.24 V and 1.8 Vare output through a linear power supply. In addition, the communicationmodule 124 can reserve 2 groups of 60-pin extension interfaces fordebugging.

FIG. 5 schematically shows an exemplary display image of the graphicaluser interface of the display module 122. The display module 122 can bedesigned using the QT programming environment. The displayed content maycomprise the monitoring results of the signal, the real-time waveform ofthe signal, the warning of signal abnormalities and similar situations,and the user interaction area, etc., so as to realize the visualinteraction between the apparatus for identifying a signal 110 and theuser. The user can set the frequency of acquiring the signal, the timeof acquiring the signal and other parameters through the userinteraction area. The display module 122 implements simple configurationaccording to the parameters set by the user, and obtains data from theserial port to realize automatic identification of the serial port. Theobtained signal data is sequentially identified according to the sendingrule of the hardware device. After identification, the signal data isstored and the signal waveform and other graphics are drawn in realtime. In addition, all signal data, signal identification result, signalwaveform and other information can be saved as a file of a predeterminedformat, such as txt, which is convenient for users to view later.

In the method, apparatus, computing device and computer-readable storagemedium for identifying a signal according to the embodiment of thepresent application, the demodulated information (for example,time-domain signal and/or frequency-domain information) is obtainedaccording to the echo signal of the transmission signal, and the trainedtarget network model is used to identify the demodulated information.This model can reduce the amount of parameters of the target networkmodel, reduce the computational load of the target network model, andtherefore improve the efficiency of signal identification. At the sametime, the signal identification result can be displayed on the graphicaluser interface, which is convenient for users to understand relevantinformation and may optimize user experience.

It should be noted that the predetermined signal frequency used by thesignal processing module 126 and the communication module 124 can be setbased on the type of the object to be measured, the processorperformance of the signal processing module 126 and the communicationmodule 124, the application scenarios of the apparatus for identifying asignal 110, etc, which are not limited.

FIG. 6a-6c schematically show a flowchart of a method for identifying asignal according to an embodiment of the present application. In someembodiments, as shown in FIG. 6a , the step S40 comprises performingquadrature demodulation on the echo signal to obtain a time-domainsignal (step S41). In addition, the step S50 comprises identifying thetime-domain signal by using the target network model to obtain theidentification result of the echo signal (step S51). In otherembodiments, as shown in FIG. 6b , the step S40 comprises performingquadrature demodulation on the echo signal to obtain a time-domainsignal (step S41) and performing a Fast Fourier Transform on thetime-domain signal to obtain frequency-domain information (Step S42). Inaddition, the step S50 comprises identifying the time-domain signal andthe frequency-domain information by using the target network model toobtain the identification result of the echo signal (step S52). In otherembodiments, as shown in FIG. 6c , the step S40 comprises performingquadrature demodulation on the echo signal to obtain a time-domainsignal (step S41) and performing a Fast Fourier Transform on thetime-domain signal to obtain frequency-domain information (step S42).Meanwhile, the step S50 comprises identifying the frequency-domaininformation by using the target network model to obtain theidentification result of the echo signal (step S53). According to theforegoing embodiments, it can be seen that there may be three specificimplementations for the step S50 of identifying the demodulatedinformation by using a target network model to obtain an identificationresult of the echo signal, that is, identifying only the time-domainsignal, identifying both the time-domain signal and the frequency-domaininformation, and identifying only the frequency-domain information. Insome other parts of this application, these three implementations canalso be expressed as: identifying the time-domain signal and/or thefrequency-domain information by using a target network model to obtainan identification result of the echo signal.

In some embodiments, steps S41 and S42 may be implemented by the echosignal demodulating module 118 of the signal processing module 126. Inother words, the echo signal demodulating module 118 can be used toperform quadrature demodulation on the echo signal to obtain atime-domain signal, and in some cases, can also be used to perform aFast Fourier Transform on the time-domain signal to obtainfrequency-domain information.

In some embodiments, the processor 102 is used to perform quadraturedemodulation on the echo signal to obtain a time-domain signal and toperform a Fast Fourier Transform on the time-domain signal to obtainfrequency-domain information.

Specifically, after receiving the radio frequency echo signal andconverting the radio frequency echo signal into an intermediatefrequency echo signal, the quadrature demodulation is performed on theintermediate frequency echo signal. The demodulation process comprisesmixing the intermediate frequency echo signal and the local oscillatorsignal, and inputting the resulting signal into the cascaded FIRlow-pass filter to obtain two orthogonal signal components, that is, thetime-domain signal. In some embodiments, Fast Fourier Transform isperformed on the time-domain signal to obtain the frequency-domaininformation of the echo signal of the measured object.

FIG. 7 schematically shows the process of obtaining time-domain signaland frequency-domain information based on the intermediate frequencyecho signal. As shown in FIG. 7, the radio frequency echo signal isdemodulated to obtain an intermediate frequency echo signal of 30 MHz to300 MHz (for example, 140 MHz), and then the intermediate frequency echosignal is mixed with the local oscillator signal and the mixed signal isinput into the cascaded FIR low-pass filter to obtain two orthogonalsignal components, namely the time-domain signal. Then, the time-domainsignal is input into the Fast Fourier Transform module for performingthe Fast Fourier Transform to obtain the frequency-domain information ofthe echo signal of the measured object.

Furthermore, according to the requirements of the target network model,after demodulating the echo signal, the time-domain signal can be outputsolely, the frequency-domain information can also be output solely, orthe time-domain signal and the frequency-domain information can beoutput together, as the input data of the target network model, which isnot limited here.

FIG. 8 schematically shows a flowchart of a method for identifying asignal according to an embodiment of the present application. As shownin FIG. 8, in some embodiments, the target network model comprises alightweight neural network model and a classifier, and the step S50comprises:

at step S501: inputting the demodulated information into the lightweightneural network model to obtain feature data; and

at step S502: inputting the feature data into the classifier to obtainthe identification result.

In some embodiments, the steps S501 and S502 may be implemented by theidentification module 120. That is, the identification module 120 can beused to input the time-domain signal and/or frequency-domain informationinto the lightweight neural network model to obtain feature data, andinput the feature data into the classifier to obtain the identificationresult.

In some embodiments, the processor 102 is used to input the time-domainsignal and/or frequency-domain information into a lightweight neuralnetwork model to obtain feature data, and to input the feature data intoa classifier to obtain an identification result.

Specifically, the trained target network model can be used to identifythe time-domain signal and/or frequency-domain information. The targetnetwork model comprises a lightweight neural network model and aclassifier. The lightweight neural network model is used to process thetime-domain signal and/or frequency-domain information to output featuredata. The classifier is used to process the feature data to output theidentification result.

The network structure of a conventional deep learning algorithm isrelatively complicated. The quantity of the network parameters and thecomplexity of the model are relatively high. When deploying algorithminto hardware in this application, a lightweight neural network model isused. The lightweight neural network model can automatically extract thefeatures of signal data, and the classifier can use the classificationalgorithm in ensemble learning to realize the classification function.Examples of classifier models comprise but are not limited to xgboostnetwork, long short-term memory (LSTM) network, gated recurrent unit(GRU), time delay neural network (TDNN), convolutional neural network(CNN), random forest classifier, LightGBM classifier, etc.

In the training process, the lightweight neural network model and theclassifier can be trained separately or together. First, the samplesignal data is input into the untrained lightweight network fortraining. During the training process, the output data of thelightweight network being trained is sample feature prediction data.After the training is completed, the trained lightweight network outputssample feature data. At this point, compared to the original inputsignal data, the feature data is more abstract and has a lowerdimension.

In some embodiments, xgboost is selected as the classifier of the entiremodel, because the xgboost algorithm has a better classification effecton low-dimensional data than deep learning. After the sample featureextraction is completed, the sample feature data is input into thexgboost algorithm model for training. After the training is completed, aclassifier with higher identification accuracy can be obtained.

In this way, it is possible to ensure the accuracy of signalidentification while improving the efficiency of signal identification.

FIG. 9 schematically shows a flowchart of a process of training alightweight neural network model according to an embodiment of thepresent application. As shown in FIG. 9, in some embodiments, thelightweight neural network model is trained by the following steps:

at step S705, obtaining sample signal data and labels corresponding tothe sample signal data;

at step S710, inputting the sample signal data into an untrainedlightweight neural network model for a first training of supervisedlearning to obtain sample feature prediction data;

at step S715, determining a first loss function based on the samplefeature prediction data and the labels corresponding to the samplesignal data;

at step S720, performing one or more first iterations on the untrainedlightweight neural network model according to a first loss datacalculated by the first loss function;

at step S725, in response to a number of the first iterations reaching afirst preset number, stopping the first iterations to obtain thelightweight neural network model.

In some embodiments, the above steps may be implemented by theidentification module 120. That is, the identification module 120 can beused to obtain sample signal data and labels corresponding to the samplesignal data; input the sample signal data into an untrained lightweightneural network model for a first training of supervised learning toobtain sample feature prediction data; determine a first loss functionbased on the sample feature prediction data and the labels correspondingto the sample signal data; perform one or more first iterations on theuntrained lightweight neural network model according to a first lossdata calculated by the first loss function; and in response to a numberof the first iterations reaching a first preset number, stopping thefirst iterations to obtain the lightweight neural network model.

In some embodiments, the above steps may be implemented by the processor102. That is, the processor 102 can be used to obtain sample signal dataand labels corresponding to the sample signal data; input the samplesignal data into an untrained lightweight neural network model for afirst training of supervised learning to obtain sample featureprediction data; determine a first loss function based on the samplefeature prediction data and the labels corresponding to the samplesignal data; perform one or more first iterations on the untrainedlightweight neural network model according to a first loss datacalculated by the first loss function; and in response to a number ofthe first iterations reaching a first preset number, stopping the firstiterations to obtain the lightweight neural network model.

Specifically, the lightweight neural network model can be implemented bydepthwise separable convolution. Compared with the standard convolution,the depthwise separable convolution reduces the calculation amount andparameter amount of the network model by changing the calculation methodof the convolution. Depthwise separable convolution decomposes theconventional convolutional layers into depthwise convolution andpointwise convolution. Depthwise convolution is a channel-basedconvolution operation, and each convolution kernel (also called asfilter) corresponds to an input channel. The pointwise convolution uses1×1 convolution kernels to merge the input channels.

FIG. 10 schematically shows a network structure of a target networkmodel according to an embodiment of the present application. In someembodiments, the lightweight neural network model of the target networkmodel comprises multiple depthwise separable sub-networks. FIG. 10 showsan embodiment in which the lightweight neural network model comprisesfour depthwise separable sub-networks. As shown in FIG. 10, thelightweight neural network model comprises a first depthwise separablesub-network 61, a second depthwise separable sub-network 62, a thirddepthwise separable sub-network 63, and a fourth depthwise separablesub-network 64.

Each depthwise separable sub-network may comprise a one-dimensionaldepthwise separable convolutional layer, a max pooling layer, and abatch normalization layer. In the one-dimensional depthwise separableconvolutional layer 611, the one-dimensional depthwise separableconvolutional layer 621, the one-dimensional depthwise separableconvolutional layer 631, and the one-dimensional depthwise separableconvolutional layer 641, the channel-based depthwise convolutionoperation is first performed. For example, a convolution processing(e.g., a first convolution processing) is performed on the windowedfeature map data sub-stream by using a windowed weight data sub-stream(e.g., a first windowed weight data sub-stream) to obtain anintermediate data stream of a plurality of channels. The first windowedweight data sub-stream is a windowed convolution kernel obtained bywindowing the weight data stream, which has a first quantity ofchannels. The windowed feature map data sub-stream is a windowed datastream obtained by windowing the feature map data stream, which has athird quantity of channels. In depthwise convolution, the third quantityis equal to the first quantity, that is, the number of channels of thefeature map data stream and the number of convolutional layers of theweight data stream are the same. Each convolution kernel corresponds toan input channel. After depthwise convolution, the windowed feature mapdata sub-stream can be converted into an intermediate data stream of aplurality of channels. Then, in the pointwise convolution, 1×1convolution kernels are used in the convolution with the correspondingintermediate data stream, and the resulting maps are merged.Specifically, the signal data to be identified, which is acquired by thelightweight neural network model, is one-dimensional sequence data, withthe corresponding channel number of 1. Therefore, in the one-dimensionaldepthwise separable convolutional layer 611 of the first depthwiseseparable convolution sub-network 61, the single-channel depthwiseconvolution operation is performed first, and then the pointwiseconvolution is performed. Since the number of input channel is 1, it canbe considered that the feature data before and after the pointwiseconvolution are the same. Then, the number of input channels of theone-dimensional depthwise separable convolutional layer 621 is thenumber of output channels of the max pooling layer of the firstdepthwise separable convolutional sub-network 61. By analogy, for twoadjacent one-dimensional depthwise separable convolutional layers, thenumber of input channels of the later one-dimensional depthwiseseparable convolutional layer is the number of the output channels ofthe max pooling layer of the former one-dimensional depthwise separableconvolutional layer.

The depthwise separable convolution reduces the amount of calculationand parameters of the network model without affecting the featureextraction effect by changing the convolution calculation method.Therefore, a lightweight neural network model can be generated by usingthe depthwise separable convolutional layer.

During training, the sample signal data obtained by the untrainedlightweight neural network model can comprises the signal data that hasbeen obtained. The labels corresponding to the sample signal data caninclude the actual category corresponding to the signal data that hasbeen obtained. In addition, the sample signal data and the labelcorresponding to the sample signal data can also be obtained from theopen source data set.

In some embodiments, the sample signal data is the signal data of vitalsigns of the living body, such as the heart rate signal data. The heartrate signal data may be a one-dimensional sequence of heart rate datawithin a period of time that has been collected according to a certaincollection frequency. The labels corresponding to the sample signal dataare normal, atrial fibrillation, other abnormal rhythms, noise, etc.

In other embodiments, if the sample signal data does not have acorresponding label, it can be manually labeled. For example, a certainamount of heart rate signal data can be randomly selected as samplesignal data from the obtained heart rate signal data, and then thecategory corresponding to each heart rate signal data can be labeled togenerate the heart rate signal data and the labels corresponding to theheart rate signal data.

Further, after the sample signal data is collected, the sample signaldata can be pre-processed, such as denoising, normalization, and cuttingthe sample signal data into predetermined lengths. Then, the samplesignal data is distributed to the training set and test set of thelightweight neural network model according to a predetermined ratio. Thepredetermined length of the sample signal data can be determinedaccording to parameters such as the type, structure, and application ofthe lightweight neural network model. The predetermined ratio can bedetermined according to parameters such as the type and structure of thelightweight neural network model. For example, it can be 6:4, 7:3, or5:5, etc., which is not specifically limited. The training set is usedto train the lightweight neural network model, and the test set is usedto optimize the lightweight neural network model.

It can be understood that, the more the number of sample signal data,the more accurate the training result of the model would be, but at thesame time, the longer the training time would be. Specifically, theappropriate number of sample signal data can be determined according tofactors such as the application scenario of the lightweight networkneural model and the user's needs, such as 1000, 3000, 5000, 8000,10000, etc.

Then, the sample signal data and the labels corresponding to samplesignal data are used to perform a training of supervised learning on theuntrained lightweight neural network model to obtain a pre-trainedlightweight neural network model. The lightweight neural network modelcan comprise one-dimensional depthwise separable convolutional layers,maximum pooling layers, batch normalization layers, global averagepooling layers, full connection layers, etc. Furthermore, one or moreresidual structures (e.g., residual blocks) can be added to thelightweight neural network model to improve the accuracy of the signaldetection of the lightweight neural network model. The residualstructure means that the input of a node can be both of the output ofthe immediate previous depthwise separable sub-network of the node andthe output of another previous depthwise separable sub-network besidesto the immediate previous one. For example, as shown in FIG. 10, theinput of the node “+” is both the output of the second depthwiseseparable sub-network 62 and the output of the fourth depthwiseseparable sub-network 64.

When training the lightweight neural network model, the sample signaldata in the training set is input into the untrained lightweight neuralnetwork model for training. The optimizer for network training is set toRMSprop, and the learning rate is 1e-4. The loss function is thecross-entropy loss function, which is specifically:

$C = {{- \frac{1}{n}}{\sum\limits_{x}\left\lbrack {{ylna} + {\left( {1 - y} \right){\ln\left( {1 - a} \right)}}} \right\rbrack}}$

where x can represent the input sample signal data; y can represent thepredicted value of the lightweight neural network model, that is, thepredicted label of the input sample signal data; a can represent theactual output value of the lightweight neural network model, that is,the actual label of the input sample signal data; and n can representthe number of samples in the training set. The loss of eachconvolutional layer is calculated according to the loss function, andthe weight of each convolutional layer is updated throughbackpropagation.

In this way, the weights of the output of the convolutional layer areupdated by using the data in the backpropagation between theconvolutional layers, such that the lightweight neural network model istrained and optimized, which can improve the accuracy of signalidentification.

As shown in FIG. 9, in some embodiments, the process of training thelightweight neural network model according to the embodiments of thepresent application further comprises: performing one or more firstiterations on the untrained lightweight neural network model accordingto a first loss data calculated by the first loss function (step S720),and in response to a number of the first iterations reaching a firstpreset number, stopping the first iterations to obtain the lightweightneural network model (step S725).

In some embodiments, the step S720 and the step S725 may be implementedby the identification module 120. In other words, the identificationmodule 120 can be used to, during the training of supervised learning onthe lightweight neural network model, stop the iteration after thenumber of iteration of the lightweight neural network model beingtrained reaches the first preset number, so as to obtain the trainedlightweight neural network model.

In some embodiments, the processor 102 is used to, during the trainingof supervised learning on the lightweight neural network model, stop theiteration after the number of iteration of the lightweight neuralnetwork model being trained reaches the first preset number, so as toobtain the trained lightweight neural network model.

Specifically, the lightweight neural network model performs one or moreiterations according to the loss data calculated by the loss function,and stops the iteration after the first preset number of iterations, andsaves the weight data in the lightweight neural network model. The firstpreset number may be determined according to factors such as theapplication scenario of the lightweight network neural model and user'sneeds, and is not specifically limited, and may be 38 times, 50 times,80 times, 100 times, etc., for example.

In this way, the accuracy of signal identification can be furtherimproved.

FIG. 11 schematically shows a flowchart of a process of training aclassifier according to an embodiment of the present application. Asshown in FIG. 11, in some embodiments, the classifier is trained throughthe following steps:

at step S805, obtaining sample feature data output by the lightweightneural network model;

at step S810, inputting the sample feature data into an untrainedclassifier for a second training of supervised learning to obtain asample identification prediction result;

at step S815, determining a second loss function based on the sampleidentification prediction result and the labels corresponding to thesample signal data;

at step S820, performing one or more second iterations on the untrainedclassifier according to a second loss data calculated by the second lossfunction; and

at step S825, in response to a number of the second iterations reachinga second preset number, stopping the second iterations to obtain theclassifier.

In some embodiments, the above steps may be implemented by theidentification module 120. That is to say, the identification module 120can be used to obtain sample feature data output by the lightweightneural network model; input the sample feature data into an untrainedclassifier for a second training of supervised learning to obtain asample identification prediction result; determine a second lossfunction based on the sample identification prediction result and thelabels corresponding to the sample signal data; perform one or moresecond iterations on the untrained classifier according to a second lossdata calculated by the second loss function; and in response to a numberof the second iterations reaching a second preset number, stop thesecond iterations to obtain the classifier.

In some embodiments, the processor 102 may be used to obtain samplefeature data output by the lightweight neural network model; input thesample feature data into an untrained classifier for a second trainingof supervised learning to obtain a sample identification predictionresult; determine a second loss function based on the sampleidentification prediction result and the labels corresponding to thesample signal data; perform one or more second iterations on theuntrained classifier according to a second loss data calculated by thesecond loss function; and in response to a number of the seconditerations reaching a second preset number, stop the second iterationsto obtain the classifier.

Specifically, since the pre-trained lightweight neural network model isused to extract the feature data of the input demodulated information,after the trained lightweight neural network model is obtained, thesample feature data output by the pooling layer can be obtained as theinput of the classifier, and the classifier experiences a training ofsupervised learning by using the sample feature data and thecorresponding label (i.e., the label corresponding to the sample signaldata that corresponds to the sample feature data) to obtain a trainedclassifier. In some embodiments, the pooling layer may be a globalpooling layer.

Further, the training parameters of the classifier, such as the type,number, and loss function of the classifier, can be set in order totrain the classifier. The classifier can be the extreme gradientboosting (xgboost) classifier which takes the Classification AndRegression Trees (CART) model as the tree model, or other integratedclassifiers, such as random forest or Gradient Boosting Decision Tree(GBDT), etc., which is not limited.

In some embodiments, the sample signal data is the signal data of thevital signs of the living body, such as heart rate signal data, and thelabel corresponding to the sample signal data is the state of the organsof the living body represented by the vital signs, such as normal,atrial fibrillation, other abnormal rhythms, noise, etc. The heart ratesignal data is input into the untrained lightweight neural network modelfor supervised learning. After the first preset number of iterations,the trained lightweight neural network model is obtained. Then, theheart rate feature data of the heart rate signal data is obtained fromthe global pooling layer of the trained lightweight neural networkmodel, and the untrained classifier experience a training of supervisedlearning according to the heart rate feature data and the labelscorresponding to the heart rate signal data to obtain the trainedclassifier.

In this way, the accuracy of the classification result can be improved,and the efficiency of signal identification can be improved whileensuring the accuracy of the signal identification.

As shown in FIG. 11, in some embodiments, the training process of theclassifier further comprises performing one or more second iterations onthe untrained classifier according to a second loss data calculated bythe second loss function (step S820) and in response to a number of thesecond iterations reaching a second preset number, stopping the seconditerations to obtain the classifier (step S825).

In some embodiments, the above steps may be implemented by theidentification module 120. In other words, the identification module 120can be used in the training of supervised learning of the classifier,and to stop the iteration after the iteration of the classifier reachesthe second preset number to obtain a trained classifier.

In some embodiments, the processor 102 is used to stop the iterationafter the iteration of the classifier reaches the second preset numberto obtain a trained classifier.

Specifically, the iteration of the classifier is stopped after thenumber of the iteration reaches the second preset number, and the weightdata in the classifier is saved. The second preset number can bedetermined according to factors such as the application scenario of theclassifier and user's needs, and is not specifically limited. Forexample, it can be 38 times, 50 times, 80 times, 100 times, etc.

In this way, the accuracy of signal identification can be furtherimproved.

FIG. 12 schematically shows the transplantation process of the trainedtarget network model. After a first preset number of iterations of thelightweight neural network model and a second preset number ofiterations of the classifier, it can be considered that the targetnetwork model has been trained. The training of the target network modelcan be done on the server side. Then, the parameters such as the savedweight data and other parameters can be transplanted to the hardwaredevice. In the hardware device, the target network model can be built,the trained parameters can be loaded, and the hardware acceleration andother operations can be performed.

In some embodiments, the identified signal is heart rate. Specifically,the sample signal data is the heart rate signal data, and the labelscorresponding to the sample signal data are normal, atrial fibrillation,other abnormal rhythms, and noise, etc. The sample heart rate signaldata and the corresponding labels are input into the untrainedlightweight neural network model for training of supervised learning.After the first preset number of iterations, the trained lightweightneural network model is obtained, and the weight data in the lightweightneural network model are saved. The sample heart rate feature data ofthe heart rate signal data is obtained from the (global) pooling layerof the trained lightweight neural network model, and the classifierexperience a training of supervised learning according to the sampleheart rate feature data and the label corresponding to the sample heartrate signal data. After a preset number of iterations, the trainedclassifier is obtained, and the weight data in the classifier is saved.The weight data in the lightweight neural network model and the weightdata in the classifier are loaded into the target network modelconstructed in the hardware device, in order to perform operations suchas signal acquisition, signal processing, hardware acceleration, andsignal identification, and to output the label (i.e., the categories)corresponding to the signal.

In this way, it is possible to ensure the accuracy of signalidentification while improving the efficiency of signal identification.

FIG. 13 schematically shows a flowchart of a method for identifying asignal according to an embodiment of the present application. As shownin FIG. 15, in some embodiments, the step S50 comprises:

at step S905, receiving a control instruction;

at step S910, receiving and caching a weight data stream and a featuremap data stream according to the control instruction;

at step S915, windowing the weight data stream to obtain a firstwindowed weight data sub-stream of a first quantity of channels and asecond windowed weight data sub-stream of a second quantity of channels;

at step S920, windowing the feature map data stream to obtain a windowedfeature map data sub-stream of a third quantity of channels, wherein thethird quantity is equal to the first quantity;

at step S925, performing a first convolution processing on the windowedfeature map data sub-stream by using the first windowed weight datasub-stream to obtain an intermediate data stream of a plurality ofchannels;

at step S930, performing a second convolution processing on theintermediate data stream of the plurality of channels by using thesecond windowed weight data sub-stream to obtain an output data stream;and

at step S935, generating the feature data based on the output datastream.

In some embodiments, the steps S905 to the step S935 may be implementedby the identification module 120. That is, the identification module 120can be used to receive the control instruction from the serial controlunit 1161, receive and cache the weight data stream and the feature mapdata stream according to the control instruction, window the weight datastream to obtain a first windowed weight data sub-stream of a firstquantity of channels and a second windowed weight data sub-stream of asecond quantity of channels, window the feature map data stream toobtain a windowed feature map data sub-stream of a third quantity ofchannels wherein the third quantity is equal to the first quantity,perform a first convolution processing on the windowed feature map datasub-stream by using the first windowed weight data sub-stream to obtainan intermediate data stream of a plurality of channels, perform a secondconvolution processing on the intermediate data stream of the pluralityof channels by using the second windowed weight data sub-stream toobtain an output data stream, and generate the feature data based on theoutput data stream.

In some embodiments, the processor 102 is used to receive the controlinstruction from the serial control unit 1161, receive and cache theweight data stream and the feature map data stream according to thecontrol instruction, window the weight data stream to obtain a firstwindowed weight data sub-stream of a first quantity of channels and asecond windowed weight data sub-stream of a second quantity of channels,window the feature map data stream to obtain a windowed feature map datasub-stream of a third quantity of channels wherein the third quantity isequal to the first quantity, perform a first convolution processing onthe windowed feature map data sub-stream by using the first windowedweight data sub-stream to obtain an intermediate data stream of aplurality of channels, perform a second convolution processing on theintermediate data stream of the plurality of channels by using thesecond windowed weight data sub-stream to obtain an output data stream,and generate the feature data based on the output data stream.

FIG. 14 schematically shows a block diagram of the internal structure ofthe identification module. As shown in FIG. 14, the serial control unit1161 sends the control instruction (CMD), weight data stream and featuremap data stream to the parallel acceleration unit 1162, and receives theoutput data stream returned by the parallel acceleration unit, andactivates and pools the output data stream. Furthermore, the serialcontrol unit can also generate an updated feature map data stream afteractivating and pooling the output data stream, and send the updatedfeature map data stream to the parallel acceleration unit for one ormore iterations.

The parallel acceleration unit receives the control instruction from theserial control unit, and performs the following operations according tothe control instruction: receiving and caching a weight data stream anda feature map data stream; windowing the weight data stream to obtain afirst windowed weight data sub-stream of a first quantity of channelsand a second windowed weight data sub-stream of a second quantity ofchannels; windowing the feature map data stream to obtain a windowedfeature map data sub-stream of a third quantity of channels, wherein thethird quantity is equal to the first quantity; performing a firstconvolution processing on the windowed feature map data sub-stream byusing the first windowed weight data sub-stream to obtain anintermediate data stream of a plurality of channels; performing a secondconvolution processing on the intermediate data stream of the pluralityof channels by using the second windowed weight data sub-stream toobtain an output data stream; and generating the feature data based onthe output data stream.

The feature map data stream can be the input time-domain signal and/orfrequency-domain information when it is first transmitted to theparallel acceleration unit. The feature map data stream after the firsttransmission can be the time-domain signal and/or frequency-domaininformation processed by using the convolutional neural network on theinput time-domain signal and/or frequency-domain signal. The weight datastream is the weight parameters of the convolutional neural network,which can be obtained through training.

Further, the serial control unit 1161 comprises a flow control sub-unit11611, a weight data sub-unit 11612, a pooling function sub-unit 11613,and an activation function sub-unit 11614. The parallel accelerationunit 1162 comprises an instruction control sub-unit 11621, a cachesub-unit 11622, multiple weight window generation sub-units 11624,multiple feature map window generation sub-units 11625, multipleconvolution sub-unit 11626 respectively corresponding to the weightwindow generation sub-units 11624 and the feature map window generationsub-units 11625, an output cache sub-unit 11622, and an output sub-unit11623.

In the serial control unit 1161, the flow control sub-unit 11611 is usedto send the control instruction (CMD) to the instruction controlsub-unit 11621, and the weight data sub-unit 11612 is used to send theweight data stream to the cache sub-unit 11622, the pooling functionsub-unit 11613 is used to send the feature map data stream to the cachesub-unit 11622, and the activation function sub-unit 11614 is used toactivate the output data stream of the output sub-unit 11623.

In the parallel acceleration unit 1162, the instruction control sub-unit11621 is coupled to the cache sub-unit 11622, the cache sub-unit 11622is coupled to the weight window generation sub-unit 11624 and thefeature map window generation sub-unit 11625, and the weight windowgeneration sub-units 11624 and the feature map window generationsub-units 11625 are coupled to the corresponding convolution sub-units11626, the convolution sub-units 11626 are coupled to the output cachesub-unit 11622, and the output cache sub-unit 11622 is coupled to theoutput sub-unit 11623.

Further, the instruction control sub-unit 11621 receives the controlinstruction CMD of the serial control unit, and controls the cachesub-unit 11622 based on the control instruction.

The cache sub-unit 11622 receives the control instruction CMD, receivesand caches the weight data stream and the feature map data streamaccording to the control instruction CMD, sends the weight data streamin parallel to multiple weight window generation sub-units 11624, andsends the feature map data stream in parallel to multiple feature mapwindow generation sub-units 11625. The feature map data stream andweight data stream are cached, such that the feature map data stream andthe weight data stream can be saved before being input into theconvolution sub-unit 11626 and the cached feature map data stream andweight data stream can be deleted after certain number of iterations, sothe data storage of machine learning model can be optimized.

In addition, the cache sub-unit 11622 can divide the weight data streaminto multiple channels of weight data sub-streams, and divide thefeature map data stream into multiple feature map data sub-streams. Eachchannel of weight data sub-stream is sent to the corresponding weightwindow generation sub-unit 11624, and each channel of feature map datasub-stream is sent to the corresponding feature map window generationsub-unit 11625, in order to complete the transmission of multiplechannels of weight data sub-streams and multiple channels of feature mapdata sub-streams.

The weight window generation sub-unit 11624 receives the weight datastream from the cache sub-unit 11622 and performs windowing processingon the weight data stream to obtain the windowed weight data sub-stream,including a first windowed weight data sub-stream of a first quantity ofchannels and a second windowed weight data sub-stream of a secondquantity of channels, and outputs the windowed weight data sub-stream tothe corresponding convolution sub-unit 11626. Multiple weight windowgeneration sub-units 11624 can be windowed in parallel. Windowingprocessing refers to converting the data stream into two-dimensionaldata. The window size can be set according to factors such as theapplication scenario of the lightweight network neural model and user'sneeds, such as the generated matrix data of 3*3, 5*5, 7*7, etc., whichis not specifically limited.

The feature map window generation sub-unit 11625 receives the featuremap data sub-stream from the cache sub-unit 11622, and performswindowing processing on the feature map data sub-stream. In someembodiments, filling processing may be performed to obtain windowedfeature map data sub-stream, and the windowed feature map datasub-stream is output to the corresponding convolution sub-unit 11626.Multiple feature map window generation sub-units 11625 can be filled andwindowed in parallel. The filling processing includes processes such asfilling 0. The convolution sub-unit 11626 performs a first convolutionprocessing on the windowed feature map data sub-stream by using thefirst windowed weight data sub-stream to obtain an intermediate datastream. The output cache sub-unit 11622 performs a second convolutionprocessing on the intermediate data stream of the plurality of channelsby using the second windowed weight data sub-stream to obtain an outputdata stream which is output to the output sub-unit 11623. Generating theoutput data stream according to the intermediate data stream comprises:accumulating the intermediate data stream to obtain the output datastream. When the input feature map data stream includes multiplechannels, the output data stream is obtained by accumulating theintermediate data stream obtained by convolution of each channel. Then,whether the processing of the data of the current convolutional layerhas finished is determined. When the processing is finished, the datacached by the output cache sub-unit is output to the output sub-unit.The output sub-unit 11623 outputs the output data stream to the serialcontrol unit 1161. In the context of this application, the terms “unit”and “sub-unit” can be understood as “circuit” and “sub-circuit”.

In this way, the amount of parameters of the target network model can bereduced, the computational load of the target network model can bereduced, and the efficiency of signal identification can be improved. Atthe same time, the signal identification result can be displayed on thegraphical user interface, which is convenient for users to understandrelevant information and may optimize user experience.

FIG. 15 schematically shows the power circuit design of theidentification module. As shown in FIG. 15, in some embodiments, theinput voltage of the identification module 116 is 12 V. The 12 V inputvoltage can be provided by an external power supply and converted into 5V, 3.3 V, 1.2 V, 1.8 V and other voltages respectively after voltageconversion, which can be used as core power supply, input power supply,output power supply and digital interface power supply.

FIG. 16 schematically shows the circuit design of the peripheralcomponent interconnect express (PCIE) standard of the identificationmodule. As shown in FIG. 16, the input and output interface of theidentification module 120 can be in the form of PCIEx8, and can beconnected to the host through the interface to complete the high-speedtransmission of input data and output data of the convolutional neuralnetwork to ensure the stability and real-time performance of datainteraction.

The apparatus for identifying a signal according to the embodiment ofthe present application includes a signal processing module 126, acommunication module 124, an identification module 120, and a displaymodule 122. Among them, the frequency-modulated signal generation module1141 of the signal processing module 126 is used to generate a modulatedsignal. The transmission signal generating module 112 of thecommunication module 124 is used to demodulate the modulated signal togenerate a transmission signal. The signal transmitting module 114 isused to transmit the transmission signal, and the echo signal receivingmodule 116 is used to receive the echo signal of the transmissionsignal. The echo signal demodulating module 118 of signal processingmodule 126 is used to demodulate the echo signal to obtain demodulatedinformation. The identification module 120 is used to identify thetime-domain signal and/or frequency-domain information by using thetrained target network model to obtain the identification result. Thedisplay module 122 is used to output the identification result to thegraphical user interface for display.

Further, the demodulation unit 1142 is used to perform quadraturedemodulation on the echo signal to obtain a time-domain signal, andperform Fast Fourier Transform on the time-domain signal to obtainfrequency-domain information.

The identification module 120 comprises a serial control unit 1161 and aparallel acceleration unit 1162. The serial control unit 1161 is used tocontrol the parallel acceleration unit 1162. The serial control unit1161 comprises a flow control sub-unit 11611, a weight data sub-unit11612, a pooling function sub-unit 11613 and an activation functionsub-unit 11614. The parallel acceleration unit 1162 is used to implementparallel convolution calculations. The parallel acceleration unit 1162comprises an instruction control sub-unit 11621, a cache sub-unit 11622,a weight window generation sub-unit 11624, a feature map windowgeneration sub-unit 11625, a convolution sub-unit 11626, an output cachesub-unit 11627, and an output sub-unit 11623.

The identification module 120 also comprises a classification unit,which is used to classify and identify the feature data to obtain anidentification result. The classification unit can classify and identifythe extracted feature data to improve the accuracy of the classificationresults, thereby improving the efficiency of signal identification whileensuring the accuracy of signal detection.

The apparatus for identifying a signal 110 according to the embodimentof this application uses the method for identifying a signal of any ofthe above embodiments to obtain the time-domain signal and/orfrequency-domain information from the echo signal of the transmissionsignal, and identify the time-domain signal and/or frequency-domaininformation by using a trained target network model, which can reducethe number of parameters of the target network model, reduce thecomputational load of the target network model, and improve theefficiency of signal identification. At the same time, the signalidentification result can be displayed on the graphical user interface,which is convenient for users to learn the relevant information and mayoptimize user experience.

The embodiment of the present application also provides a non-volatilecomputer-readable storage medium storing thereon computer programs orinstructions. When the computer programs or instructions are executed byone or more processors, the method for identifying a signal described inany of the above embodiments would be implemented.

In the description of this specification, the description with referenceto the terms “an embodiment”, “some embodiments”, “exemplaryembodiments”, “examples”, “specific examples”, or “some examples”, etc.means the specific features, structures, materials, or characteristicsdescribed in combination with the embodiments or the examples areincluded in at least one embodiment or example of the presentapplication. In this specification, the schematic expression of theabove-mentioned terms does not necessarily refer to the same embodimentor example. Moreover, the described specific features, structures,materials or characteristics can be combined in an appropriate manner inany one or more embodiments or examples.

A person of ordinary skill in the art can understand that all or part ofthe steps carried in the method of the foregoing embodiments can beimplemented by relevant hardware instructed by a program. The programcan be stored in a computer-readable storage medium. When the program isexecuted, one or more steps of the method according to the embodimentswill be implemented.

In addition, each functional unit in each embodiment of the presentapplication may be integrated into one processing module, or may existalone physically, or two or more units may be integrated into onemodule. The above-mentioned integrated unit or module can be implementedin the form of hardware or the form of software functional unit ormodule. If the integrated unit or module is implemented in the form of asoftware functional module or unit and is sold or used as an independentproduct, it can also be stored in a computer readable storage medium.

The storage medium mentioned above can be a read-only memory, a magneticdisk or an optical disk, etc.

Although the embodiments of the present application have been shown anddescribed, those of ordinary skill in the art can understand thatvarious changes, modifications, substitutions and deformations can bemade to these embodiments without departing from the principle andpurpose of the present application. The scope of the application isdefined by the claims and their equivalents.

I claim:
 1. A method for identifying a signal, comprising: demodulating a modulated signal to generate a transmission signal; transmitting the transmission signal; receiving an echo signal generated by a reflection of the transmission signal; demodulating the echo signal to obtain demodulated information; identifying the demodulated information by using a target network model to obtain an identification result of the echo signal; and outputting the identification result to a graphical user interface for display.
 2. The method according to claim 1, wherein demodulating the echo signal to obtain the demodulated information comprises: performing quadrature demodulation on the echo signal to obtain a time-domain signal, and wherein identifying the demodulated information by using the target network model to obtain the identification result of the echo signal comprises: identifying the time-domain signal by using the target network model to obtain the identification result of the echo signal.
 3. The method according to claim 1, wherein demodulating the echo signal to obtain the demodulated information comprises: performing quadrature demodulation on the echo signal to obtain a time-domain signal; and performing a Fast Fourier Transform on the time-domain signal to obtain frequency-domain information, and wherein identifying the demodulated information by using the target network model to obtain the identification result of the echo signal comprises: identifying the time-domain signal and the frequency-domain information by using the target network model to obtain the identification result of the echo signal.
 4. The method according to claim 1, wherein demodulating the echo signal to obtain the demodulated information comprises: performing quadrature demodulation on the echo signal to obtain a time-domain signal; and performing a Fast Fourier Transform on the time-domain signal to obtain frequency-domain information, and wherein identifying the demodulated information by using the target network model to obtain the identification result of the echo signal comprises: identifying the frequency-domain information by using the target network model to obtain the identification result of the echo signal.
 5. The method according to claim 1, wherein the target network model comprises a lightweight neural network model and a classifier, and wherein identifying the demodulated information by using the target network model to obtain the identification result of the echo signal comprises: inputting the demodulated information into the lightweight neural network model to obtain feature data; and inputting the feature data into the classifier to obtain the identification result.
 6. The method according to claim 5, wherein the lightweight neural network model is trained by operations comprising: obtaining sample signal data and labels corresponding to the sample signal data; inputting the sample signal data into an untrained lightweight neural network model for a first training of supervised learning to obtain sample feature prediction data; determining a first loss function based on the sample feature prediction data and the labels corresponding to the sample signal data; performing one or more first iterations on the untrained lightweight neural network model according to a first loss data calculated by the first loss function; and in response to a number of the first iterations reaching a first preset number, stopping the first iterations to obtain the lightweight neural network model.
 7. The method according to claim 6, wherein the classifier is trained by operations comprising: obtaining sample feature data output by the lightweight neural network model; inputting the sample feature data into an untrained classifier for a second training of supervised learning to obtain a sample identification prediction result; determining a second loss function based on the sample identification prediction result and the labels corresponding to the sample signal data; performing one or more second iterations on the untrained classifier according to a second loss data calculated by the second loss function; and in response to a number of the second iterations reaching a second preset number, stopping the second iterations to obtain the classifier.
 8. The method according to claim 5, wherein inputting the demodulated information into the lightweight neural network model to obtain the feature data comprises: receiving a control instruction; receiving and caching a weight data stream and a feature map data stream according to the control instruction; windowing the weight data stream to obtain a first windowed weight data sub-stream of a first quantity of channels and a second windowed weight data sub-stream of a second quantity of channels; windowing the feature map data stream to obtain a windowed feature map data sub-stream of a third quantity of channels, wherein the third quantity is equal to the first quantity; performing a first convolution processing on the windowed feature map data sub-stream by using the first windowed weight data sub-stream to obtain an intermediate data stream of a plurality of channels; performing a second convolution processing on the intermediate data stream of the plurality of channels by using the second windowed weight data sub-stream to obtain an output data stream; and generating the feature data based on the output data stream.
 9. An apparatus for identifying a signal, comprising: a transmission signal generating module, configured to demodulate a modulated signal to generate a transmission signal; a signal transmitting module, configured to transmit the transmission signal; an echo signal receiving module, configured to receive an echo signal generated by a reflection of the transmission signal; an echo signal demodulating module, configured to demodulate the echo signal to obtain demodulated information; an identification module, configured to identify the demodulated information by using a target network model to obtain an identification result of the echo signal; and a display module, configured to output the identification result to a graphical user interface for display.
 10. The apparatus according to claim 9, wherein the echo signal demodulating module is configured to perform quadrature demodulation on the echo signal to obtain a time-domain signal.
 11. The apparatus according to claim 10, wherein the echo signal demodulating module is configured to perform a Fast Fourier Transform on the time-domain signal to obtain frequency-domain information.
 12. The apparatus according to claim 9, wherein the identification module comprises a serial control unit and a parallel acceleration unit, and wherein the serial control unit is configured to control the parallel acceleration unit, and the parallel acceleration unit is configured to achieve a parallel convolution calculation.
 13. The apparatus according to claim 12, wherein the serial control unit comprises a flow control sub-unit, a weight data sub-unit, a pooling function sub-unit, and an activation function sub-unit.
 14. The apparatus according to claim 12, wherein the parallel acceleration unit comprises an instruction control sub-unit, a cache sub-unit, a weight window generation sub-unit, a feature map window generation sub-unit, a convolution sub-unit, an output cache sub-unit, and an output sub-unit.
 15. The apparatus according to claim 11, wherein the identification module further comprises a classification unit, and wherein the classification unit is configured to identify the demodulated information to obtain the identification result of the echo signal.
 16. A computing device, comprising: a memory configured to store computer-executable instructions; and a processor configured to execute the computer-executable instructions to cause the computing device to perform the method according to claim
 1. 17. A computer-readable storage medium, comprising computer-executable instructions that when executed by a processor of a computing device cause the processor to perform the method according to claim
 1. 